Research Article
PM10-Based Air Quality Prediction Using Machine Learning for Environmental Monitoring
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357852, author={Gudipati Sri Vibhavan and Rajasekar J and Ande Umesh Chandra and Nishith Mani Raj}, title={PM10-Based Air Quality Prediction Using Machine Learning for Environmental Monitoring}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={pm10 machine learning time-series fore-casting regression analysis industrial activities traffic flow weather factors historical data}, doi={10.4108/eai.28-4-2025.2357852} }- Gudipati Sri Vibhavan
Rajasekar J
Ande Umesh Chandra
Nishith Mani Raj
Year: 2025
PM10-Based Air Quality Prediction Using Machine Learning for Environmental Monitoring
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357852
Abstract
Machine learning methodologies such as time-series forecasting and regression analysis are employed to anticipate future PM10 concentrations, considering input variables such as meteorological conditions, traffic dynamics, and industrial operations. This study seeks to discern patterns and trends in historical data that may improve the precision of air quality forecasts. These models are intended to enhance the prediction of pollution patterns and inform the public about the possible health risks linked to high particulate matter concentrations. They intend to deliver precise, up-to-date PM10 concentration forecasts. By providing precise, localized estimates of PM10 concentrations, this method helps policymakers to develop customized plans targeted at reducing pollution exposure. By clarifying how particulate matter affects public health, the study's conclusions aid in the creation of sensible environmental policies.
